Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Valentina Macchiarulo, Pietro Milillo, Chris Blenkinsopp, Giorgia Giardina

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Abstract

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

Original languageEnglish
Pages (from-to)1849-1878
Number of pages30
JournalStructural Health Monitoring
Volume21
Issue number4
Early online date5 Jan 2022
DOIs
Publication statusPublished - 1 Jul 2022

Bibliographical note

Funding Information:
We thank the European Space Agency for providing Sentinel data over Los Angeles for this project. COSMO-SkyMed and ERS/Envisat InSAR time-series were provided by the Italian Ministry of the Environment through the Geoportale Nazionale database (http://www.pcn.minambiente.it/mattm/). Sentinel InSAR time-series over Tuscany were accessed on 1 June 2020 through the Geoportale Regione Toscana (https://geoportale.lamma.rete.toscana.it/difesa_suolo/#/viewer/openlayers/326). Landslide shapefile data were provided by ISPRA License: CC BY SA 4.0. V. Macchiarulo was supported by a PhD scholarship granted by Sue and Roger Whorrod and the Alumni programme of the University of Bath. Part of this research was carried out when P. Milillo was at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The author(s) received no financial support for the research, authorship, and/or publication of this article.

Funding Information:
We thank the European Space Agency for providing Sentinel data over Los Angeles for this project. COSMO-SkyMed and ERS/Envisat InSAR time-series were provided by the Italian Ministry of the Environment through the Geoportale Nazionale database ( http://www.pcn.minambiente.it/mattm/ ). Sentinel InSAR time-series over Tuscany were accessed on 1 June 2020 through the Geoportale Regione Toscana ( https://geoportale.lamma.rete.toscana.it/difesa_suolo/#/viewer/openlayers/326 ). Landslide shapefile data were provided by ISPRA License: CC BY SA 4.0. V. Macchiarulo was supported by a PhD scholarship granted by Sue and Roger Whorrod and the Alumni programme of the University of Bath. Part of this research was carried out when P. Milillo was at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Keywords

  • Italy
  • Los Angeles
  • MT-InSAR
  • bridges
  • critical infrastructure
  • early warning
  • infrastructure resilience
  • remote sensing
  • roadway
  • transport networks

ASJC Scopus subject areas

  • Mechanical Engineering

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